code: def _evaluate_set_function(
      set_func: Callable[[np.ndarray], Union[float, np.ndarray]],
      evaluation_subsets: Union[Set[Tuple[int]], List[Tuple[int]]],
      parallel_context: Parallel,
      show_progressbar: bool = True,
  ) -> Dict[Tuple[int], Union[float, np.ndarray]]:
      def parallel_job(
          input_subset: Tuple[int], parallel_random_seed: int
      ) -> Union[float, np.ndarray]:
          set_random_seed(parallel_random_seed)
  
          return set_func(np.array(input_subset))
  
      random_seeds = np.random.randint(
          np.iinfo(np.int32).max, size=len(evaluation_subsets)
      )
      subset_results = parallel_context(
          delayed(parallel_job)(subset_to_evaluate, int(random_seed))
          for subset_to_evaluate, random_seed in tqdm(
              zip(evaluation_subsets, random_seeds),
              desc="Evaluate set function",
              position=0,
              leave=True,
              disable=not config.show_progress_bars
              or not show_progressbar,
          )
      )
  
      subset_to_result_map = {}
      for subset, result in zip(evaluation_subsets, subset_results):
          subset_to_result_map[subset] = result
  
      return subset_to_result_map
  
imports: import itertools
  import math
  from enum import Enum
  from typing import Callable, Dict, List, Optional, Set, Tuple, Union
  import numpy
  import scipy
  from joblib import Parallel, delayed
  from scipy.special import comb
  from scipy.stats._qmc import Halton
  from sklearn.linear_model import LinearRegression
  from tqdm import tqdm
  import dowhy.gcm.config
  from dowhy.gcm.util.general import set_random_seed
label_desc: Use image node_id 10 for calling a global function with example usage: _evaluate_set_function(set_func, evaluation_subsets, parallel_context, show_progressbar) and returns: subset_to_result_map, set_func